The Biggest Differences Between BigQuery and Snowflake

Technologyby Sumona18 January 2022

BigQuery

At the center of every modern data stack, there is a cloud data warehouse. This warehouse is necessary to derive insights from your data. Without a BigQuery, you are left with serious holes in your analytics.

In the simplest terms, a data warehouse is an analytics platform where information from various data sources is stored for use. This data is used to make better business decisions and answer important questions about your business.

Data Warehousing Is The Part Of New Digital Age

In today’s digital age, nearly every company is leveraging a data warehouse or in the process of adopting one. Those who are refusing to change with the times are being left in the dust.

BigQuery and snowflakes are both parts of the new digital data warehousing technology. As someone who is new to the space, it can be extremely overwhelming trying to decipher all of the cloud-related jargon. Let this article be your guide on this topic.

What Is Snowflake?

There are several big players in the data warehousing space, but today, we will be focusing on BigQuery and Snowflake. These are the two most popular brands in the industry, representing thousands of clients each.

Snowflake is a Software-as-a-Service (SaaS) based warehouse solution that is compatible with any of the popular cloud providers like AWS, Azure, and GCP. The brand was launched publicly in 2014 and was valued at 90.35 billion in October 2021.

Though this solution was purpose-built for the cloud, Snowflake has some very unique features compared to other cloud data warehouses. For one, it comes with zero baggage and virtually no management or operational overhead.

It also handles all of the backend infrastructures, so you do not have to worry about any technical issues. The service also offers a lot of scalabilities enabling near-unlimited concurrent queries.

What Is Google BigQuery?

What Is Google BigQuery?

Google BigQuery, as you can probably guess, is part of the Google Cloud Platform. It was first launched in 2010 and was one of the first data warehouse solutions to hit the market.

When Google BigQuery was first released, many people thought of it as a complex query engine. Many of its capabilities were limited, which led to the slow growth of its user base. However, over a decade later, the platform has been revolutionized and is now a leader in the cloud service industry.

With BigQuery, you do not have to establish or maintain any infrastructure. Chances are, you are not a data specialist or engineer. When you have a reliable infrastructure that is handled for you, you can better use your time to help your company.

Structure Wise Differences Between BigQuery And Snowflakes

The BigQuery vs Snowflake debate has been prevalent for quite some time now. One of the main features that separate these two platforms are their structures. Snowflake is a completely serverless solution that uses fully separated storage from computing. It is based on ANSI SQL.

The architecture of Snowflake is based on a mix of traditional shared-disk and shared-nothing architectures, providing the user with benefits from both models. This makes your data accessible to all compute nodes in the platform by using a central repository for persisted data.

Snowflake uses MPP (massively parallel processing) to process all of its queries. As a result, each individual computer cluster (virtual machine or server) stores a portion of your entire data set locally.

For storage, Snowflake stores your data in separate micro partitions that are internally optimized and compressed into columnar storage. This keeps your information neatly organized. In fact, all data that is loaded into Snowflake is reorganized, optimized, and compressed into a columnar format in order to keep it in cloud storage.

Similar to Snowflake, Google BigQuery is serverless and uses separated storage from computing. It is also based on ANSI SQL. Yet, the architecture of this platform is very different from Snowflake.

BigQuery uses an expansive set of multi-tenant services driven by specific Google infrastructure technologies like Dremel, Colossus, Jupiter, and Borg. Google BigQuery computes values using Dremel, which is a large multi-tenant compute cluster that executes SQL queries.

Just like Snowflake, BigQuery compresses data into a columnar format. However, it stores the data in Colossus, which is Google’s global storage system. 

Scalable Differences

Snowflake provides an auto-scaling and auto suspend feature that lets clusters stop or start during busy or idle times. Under Snowflake, your users are unable to resize nodes. However, they can resize clusters with a single click.

Also, Snowflake lets you autoscale up to 10 warehouses with a limit of 20 DML per queue in a single table.

Conclusion:

Big Query uses a similar process, automatically provisioning your additional compute resources as needed. Yet, BigQuery has a limit of 100 concurrent users by default. Both of these platforms let you scale up and down automatically in accordance with demand.

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Sumona

Sumona is a persona, having a colossal interest in writing blogs and other jones of calligraphies. In terms of her professional commitments, she carries out sharing sentient blogs by maintaining top-to-toe SEO aspects. Follow more of her contributions in EmblemWealth

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